1. (a) Field of the Invention
The present invention relates to a soft decision decoder, and a log likelihood ratio calculator and method thereof for soft decision decoding. More specifically, the present invention relates to a soft decision decoder for realizing a generalized log likelihood ratio algorithm in consideration of channel estimation errors for better performance in soft decision decoding of QAM (quadrature amplitude modulation) signals, and a log likelihood ratio calculator and method thereof for soft decision decoding.
2. (b) Description of the Related Art
As information communication techniques have evolved to mobilization and multimedia stages, the QAM method has become the most effective modulation method from among the currently used modulation schemes for realizing high-speed multimedia mobile communications using restricted frequency resources.
Also, it is required to use error correction codes such as turbo codes so as to perform reliable multimedia communications under the poor mobile communication channel environments.
However, since the turbo codes need soft decision decoding, and the QAM signals carry information through phases and amplitudes, a soft decision decoding algorithm in consideration of channel estimation errors is necessary.
A conventional log likelihood ratio algorithm for soft decision decoding will now be described in detail.
A symbol x of the QAM signals in the M-ary QAM has one of M signal symbols {x1, x2, . . . , xM}, and each symbol xi is constituted by k bits of {c1, c2, . . . , ck} assuming that M=2k, the bit ci configuring the respective symbols has one of values +1 and −1, and a generation probability of +1 and −1 is respectively ½.
In general, when a QAM transmit signal x is passed through a channel state a and has noise n added thereto, and is received as y at a receiver, the receive signal y is given as Equation 1.y=ax+n  Equation 1
Since a is a channel gain from Equation 1, a has a constant value for a symbol duration of the QAM signals, and n is AWGN (additive white Gaussian noise).
When a channel estimator of the receiver estimates the channel, a channel estimation value a is given as Equation 2.â=a+e  Equation 2
From Equation 2, e is assumed to have a Gaussian distribution in consideration of channel estimation errors.
When not considering the channel estimation errors, that is, if e=0 so â=a, a log likelihood ratio for bit decision in this case is given as Equation 3.
                              γ          ⁡                      (                          c              i                        )                          =                                            ln              ⁢                                                ∑                                                            x                      +                                        ∈                                          {                                                                        x                          :                                                      c                            i                                                                          =                                                  +                          1                                                                    }                                                                      ⁢                                  exp                  ⁢                                      (                                          -                                                                                                                                                              y                              -                                                                                                a                                  ^                                                                ⁢                                                                  x                                  +                                                                                                                                                                          2                                                                          σ                          n                          2                                                                                      )                                                                        -                          ln              ⁢                                                ∑                                                            x                      -                                        ∈                                          {                                                                        x                          :                                                      c                            i                                                                          =                                                  -                          1                                                                    }                                                                      ⁢                                  exp                  ⁡                                      (                                          -                                                                                                                                                              y                              -                                                                                                a                                  ^                                                                ⁢                                                                  x                                  -                                                                                                                                                                          2                                                                          σ                          n                          2                                                                                      )                                                                                ⁢                      ≷                          -              1                                      +              1                                ⁢          1                                    Equation        ⁢                                  ⁢        3            
From Equation 3, a generalized log likelihood ratio algorithm for soft decision decoding with no consideration of the channel estimation errors is given as Equation 4.
                                          γ            ~                    ⁡                      (                          c              i                        )                          =                                                            min                                                      x                    -                                    ∈                                      {                                                                  x                        :                                                  c                          i                                                                    =                                              -                        1                                                              }                                                              ⁢                                                                                      y                    -                                                                  a                        ^                                            ⁢                                              x                        -                                                                                                              2                                      -                                          min                                                      x                    +                                    ∈                                      {                                                                  x                        :                                                  c                          i                                                                    =                                              +                        1                                                              }                                                              ⁢                                                                                      y                    -                                                                  a                        ^                                            ⁢                                              x                        +                                                                                                              2                                              ⁢                      ≷                          -              1                                      +              1                                ⁢          0                                    Equation        ⁢                                  ⁢        4            where the reference signal x+ is a symbol x including the case of ci=+1 from among the bits configuring the symbol x, and the reference signal x− is a symbol x including the case of ci=−1 from among the bits configuring the symbol x.
FIG. 1 shows a conventional configuration of a soft decision decoder of QAM signals.
As shown in FIG. 1, the soft decision decoder comprises log likelihood ratio calculators 10, a subtractor 20, and a comparator 30.
The log likelihood ratio calculator 10 calculates log likelihood ratios of (−) and (+) signals as given in Equations 3 and 4. The subtractor 20 calculates a difference of the log likelihood ratios calculated by using the (+) and (−) signals. The comparator 30 receives calculates results on the difference of the log likelihood ratios from the subtractor 20, and determines a soft decision value of the QAM signal as (+) or (−) according to comparison results of a positive number and a negative number of the difference of the log likelihood ratios.
FIG. 2 shows a block diagram of a conventional log likelihood ratio calculator for soft decision decoding.
As shown in FIG. 2, the log likelihood ratio calculator comprises a multiplier 11, a subtractor 12, a square calculator 13, and a comparator 14.
The multiplier 11 multiplies a reference signal and a channel state a with no consideration of channel estimation errors, the subtractor 12 subtracts an output signal of the multiplier 11 from a receive signal, and the square calculator 13 squares an output signal of the subtractor 12. The comparator 14 compares output signals of the square calculator 13.
FIG. 2 is a configuration of the log likelihood ratio calculator corresponding to a first term or a second term of Equation 4.
Therefore, as shown in FIG. 1, the whole configuration for soft decision decoding includes two log likelihood ratio calculators of FIG. 2, the subtractor 20 performs log subtraction, and the comparator 30 determines a soft decision value of the QAM signal as 1 when a subtraction result by the subtractor 20 is greater than 0, and determines the soft decision value of the QAM signal as −1 when a subtraction result by the subtractor 20 is less than 0.
However, since the conventional log likelihood ratio calculator for soft decision decoding does not consider channel estimation errors, the conventional log likelihood ratio calculator recovers signals while failing to completely reflect the actual channel estimation errors.
Therefore, the modulation method for the QAM signals having information be loaded to the amplitude thereof needs decoding in consideration of the channel estimation errors for the optimized signal recovery. However, since the conventional log likelihood ratio calculator does not completely reflect the channel estimation errors and performs decoding, a soft decision decoding performance on the receive QAM signals is lowered.